sfx_index

Models for serial femtosecond crystallography indexing.

Classes:

Name	Description
IndexCrystFELParameters	Perform indexing of hits/peaks using CrystFEL's indexamajig.

ConcatenateStreamFilesParameters

Bases: TaskParameters

Parameters for stream concatenation.

Concatenates the stream file output from CrystFEL indexing for multiple experimental runs.

Source code in lute/io/models/sfx_index.py

class ConcatenateStreamFilesParameters(TaskParameters):
    """Parameters for stream concatenation.

    Concatenates the stream file output from CrystFEL indexing for multiple
    experimental runs.
    """

    class Config(TaskParameters.Config):
        set_result: bool = True
        """Whether the Executor should mark a specified parameter as a result."""

    in_file: str = Field(
        "",
        description="Root of directory tree storing stream files to merge.",
    )

    tag: Optional[str] = Field(
        "",
        description="Tag identifying the stream files to merge.",
    )

    out_file: str = Field(
        "", description="Path to merged output stream file.", is_result=True
    )

    @validator("in_file", always=True)
    def validate_in_file(cls, in_file: str, values: Dict[str, Any]) -> str:
        if in_file == "":
            stream_file: Optional[str] = read_latest_db_entry(
                f"{values['lute_config'].work_dir}", "IndexCrystFEL", "out_file"
            )
            if stream_file:
                stream_dir: str = str(Path(stream_file).parent)
                return stream_dir
        return in_file

    @validator("tag", always=True)
    def validate_tag(cls, tag: str, values: Dict[str, Any]) -> str:
        if tag == "":
            stream_file: Optional[str] = read_latest_db_entry(
                f"{values['lute_config'].work_dir}", "IndexCrystFEL", "out_file"
            )
            if stream_file:
                stream_tag: str = Path(stream_file).name.split("_")[-1].split(".")[0]
                return stream_tag
        return tag

    @validator("out_file", always=True)
    def validate_out_file(cls, tag: str, values: Dict[str, Any]) -> str:
        if tag == "":
            stream_out_file: str = str(
                Path(values["in_file"]).parent / f"{values['tag']}.stream"
            )
            return stream_out_file
        return tag

Config

Bases: Config

Source code in lute/io/models/sfx_index.py

class Config(TaskParameters.Config):
    set_result: bool = True
    """Whether the Executor should mark a specified parameter as a result."""

set_result: bool = True class-attribute instance-attribute

Whether the Executor should mark a specified parameter as a result.

IndexCCTBXXFELParameters

Bases: ThirdPartyParameters

Parameters for indexing with cctbx.xfel.

Source code in lute/io/models/sfx_index.py

class IndexCCTBXXFELParameters(ThirdPartyParameters):
    """Parameters for indexing with cctbx.xfel."""

    class Config(ThirdPartyParameters.Config):
        set_result: bool = False
        """Whether the Executor should mark a specified parameter as a result."""

    class PhilParameters(BaseModel):
        """Template parameters for CCTBX phil file."""

        class Config(BaseModel.Config):
            extra: str = "allow"

        # Generic input settings: input_
        input_reference_geometry: Optional[str] = Field(
            None,
            description=(
                "Provide an models.expt file with exactly one detector model. Data "
                "processing will use that geometry instead of the geometry found "
                "in the image headers."
            ),
        )

        # Generic geometry: geometry_
        geometry_detector_panel_origin: Optional[Tuple[float, float, float]] = Field(
            None,
            description="Override the panel origin. Requires fast_axis and slow_axis.",
        )

        # Generic output settings: output_
        output_output_dir: str = Field(
            "",
            description="Directory output files will be placed",
        )
        output_composite_output: bool = Field(
            True,
            description=(
                "If True, save one set of experiment/reflection files per process, "
                "where each is a concatenated list of all the successful events "
                "examined by that process. If False, output a separate "
                "experiment/reflection file per image (generates a lot of files)."
            ),
        )
        output_logging_dir: str = Field(
            "", description="Directory output log files will be placed"
        )

        # Dispatch settings: dispatch_
        dispatch_index: bool = Field(
            True,
            description=(
                "Attempt to index images. find_spots also needs to be True for "
                "this to work"
            ),
        )
        dispatch_refine: bool = Field(
            False, description="If True, after indexing, refine the experimental models"
        )
        dispatch_integrate: bool = Field(
            True,
            description=(
                "Integrate indexed images. Ignored if index=False or "
                "find_spots=False"
            ),
        )

        # Parallel processing parameters: mp_
        mp_method: str = Field(
            "mpi",  # *multiprocessing sge lsf pbs mpi
            description="The multiprocessing method to use",
        )

        # Spotfinding parameters: spotfinder_
        spotfinder_lookup_mask: Optional[str] = Field(
            None, description="The path to the mask file."
        )
        spotfinder_threshold_dispersion_gain: Optional[float] = Field(
            None,
            description=(
                "Use a flat gain map for the entire detector to act as a "
                "multiplier for the gain set by the format. Cannot be used in "
                "conjunction with lookup.gain_map parameter."
            ),
        )
        spotfinder_threshold_dispersion_sigma_bkgnd: float = Field(
            6,
            description=(
                "The number of standard deviations of the index of dispersion "
                "(variance / mean) in the local area below which the pixel "
                "will be classified as background."
            ),
        )
        spotfinder_threshold_dispersion_sigma_strong: float = Field(
            3,
            description=(
                "The number of standard deviations above the mean in the local "
                "area above which the pixel will be classified as strong."
            ),
        )
        spotfinder_threshold_dispersion_global_threshold: float = Field(
            0,
            description=(
                "The global threshold value. Consider all pixels less than "
                "this value to be part of the background."
            ),
        )
        spotfinder_threshold_dispersion_kernel_size: Tuple[int, int] = Field(
            (6, 6),
            description=(
                "The size of the local area around the spot in which to "
                "calculate the mean and variance. The kernel is given as a box "
                "of size (2 * nx + 1, 2 * ny + 1) centred at the pixel."
            ),
        )
        spotfinder_filter_min_spot_size: Optional[int] = Field(
            3,
            description=(
                "The minimum number of contiguous pixels for a spot to be "
                "accepted by the filtering algorithm."
            ),
        )
        spotfinder_filter_d_min: Optional[float] = Field(
            None,
            description=(
                "The high resolution limit in Angstrom for a pixel to be "
                "accepted by the filtering algorithm."
            ),
        )

        # Indexing parameters: indexing_
        indexing_stills_refine_candidates_with_known_symmetry: bool = Field(
            False,
            description=(
                "If False, when choosing the best set of candidate basis "
                "solutions, refine the candidates in the P1 setting. If True, "
                "after indexing in P1, convert the candidates to the known "
                "symmetry and apply the corresponding change of basis to the "
                "indexed reflections."
            ),
        )
        indexing_stills_refine_all_candidates: bool = Field(
            True,
            description=(
                "If False, no attempt is made to refine the model from initial "
                "basis vector selection. The indexing solution with the best "
                "RMSD is chosen."
            ),
        )
        indexing_known_symmetry_space_group: Optional[str] = Field(
            None, description="Target space group for indexing."
        )
        indexing_known_symmetry_unit_cell: Optional[str] = Field(
            None, description="Target unit cell for indexing."
        )

        # Integration parameters: integration_
        integration_background_simple_outlier_plane_n_sigma: int = Field(
            10,
            description=(
                "The number of standard deviations above the threshold "
                "plane to use in rejecting outliers from background "
                "calculation."
            ),
        )
        integration_summation_detector_gain: float = Field(
            1.0,
            description=(
                "Multiplier for variances after integration of still images. See "
                "Leslie 1999."
            ),
        )

        # Profiling parameters: profile_
        profile_gaussian_rs_centroid_definition: str = Field(
            "com",
            description="The centroid to use as beam divergence (centre of mass or s1)",
        )

        # Refinement options: refinement_
        refinement_reflections_outlier_algorithm: Optional[str] = Field(
            None,
            description=(
                "Outlier rejection algorithm. If auto is selected, the "
                "algorithm is chosen automatically."
            ),
        )

        @validator("output_output_dir", always=True)
        def set_output_dir(cls, output: str, values: Dict[str, Any]) -> str:
            if output == "":
                return os.getenv("LUTE_WORK_DIR", ".")
            return output

        @validator("output_logging_dir", always=True)
        def set_output_log_dir(cls, output: str, values: Dict[str, Any]) -> str:
            if output == "":
                return values["output_output_dir"]
            return output

    _set_phil_template_parameters = template_parameter_validator("phil_parameters")

    executable: str = Field(
        "/sdf/group/lcls/ds/tools/cctbx/conda_base/bin/mpirun",
        description="MPI executable.",
        flag_type="",
    )
    cctbx_executable: str = Field(
        "/sdf/group/lcls/ds/tools/cctbx/build/bin/dials.stills_process",
        description="CCTBX indexing program (DIALS).",
        flag_type="",
    )
    in_file: str = Field(
        "",
        description=(
            "The location of a data specification for LCLS. "
            "This file will be written for you based on the data_spec parameter. "
            "If not running at LCLS, this can be an input file, or a glob."
        ),
        flag_type="",
    )
    data_spec: Optional[Dict[str, Union[str, float, int]]] = Field(
        None,
        description="Provide a CCTBX specification for data access.",
        flag_type="",
    )
    phil_file: str = Field(
        "",
        description="Location of the input settings ('phil') file.",
        flag_type="",
    )
    phil_parameters: Optional[PhilParameters] = Field(
        None,
        description="Optional template parameters to fill in a CCTBX phil file.",
        flag_type="",  # Does nothing since always None by time it's seen by Task
    )
    lute_template_cfg: TemplateConfig = Field(
        TemplateConfig(
            template_name="cctbx_index.phil",
            output_path="",
        ),
        description="Template information for the cctbx_index file.",
    )

    @validator("phil_file", always=True)
    def set_default_phil_path(cls, phil_file: str, values: Dict[str, Any]) -> str:
        if phil_file == "":
            return f"{values['lute_config'].work_dir}/cctbx_index.phil"
        return phil_file

    @validator("lute_template_cfg", always=True)
    def set_phil_template_path(
        cls, lute_template_cfg: TemplateConfig, values: Dict[str, Any]
    ) -> TemplateConfig:
        if lute_template_cfg.output_path == "":
            lute_template_cfg.output_path = values["phil_file"]
        return lute_template_cfg

    @validator("in_file", always=True)
    def set_in_file(cls, in_file: str, values: Dict[str, Any]) -> str:
        if in_file == "":
            exp: str = values["lute_config"].experiment
            run: str = str(values["lute_config"].run)
            work_dir: str = values["lute_config"].work_dir
            return f"{work_dir}/data_{exp}_{run}.loc"
        return in_file

    @validator("data_spec", always=True)
    def write_data_spec_file(
        cls,
        data_spec: Optional[Dict[str, Union[str, float, int]]],
        values: Dict[str, Any],
    ) -> None:
        if data_spec is not None:
            with open(values["in_file"], "w") as f:
                for key, value in data_spec.items():
                    spec_line: str = f"{key}={value}\n"
                    f.write(spec_line)
        return None

Config

Bases: Config

Source code in lute/io/models/sfx_index.py

class Config(ThirdPartyParameters.Config):
    set_result: bool = False
    """Whether the Executor should mark a specified parameter as a result."""

set_result: bool = False class-attribute instance-attribute

Whether the Executor should mark a specified parameter as a result.

PhilParameters

Bases: BaseModel

Template parameters for CCTBX phil file.

Source code in lute/io/models/sfx_index.py

class PhilParameters(BaseModel):
    """Template parameters for CCTBX phil file."""

    class Config(BaseModel.Config):
        extra: str = "allow"

    # Generic input settings: input_
    input_reference_geometry: Optional[str] = Field(
        None,
        description=(
            "Provide an models.expt file with exactly one detector model. Data "
            "processing will use that geometry instead of the geometry found "
            "in the image headers."
        ),
    )

    # Generic geometry: geometry_
    geometry_detector_panel_origin: Optional[Tuple[float, float, float]] = Field(
        None,
        description="Override the panel origin. Requires fast_axis and slow_axis.",
    )

    # Generic output settings: output_
    output_output_dir: str = Field(
        "",
        description="Directory output files will be placed",
    )
    output_composite_output: bool = Field(
        True,
        description=(
            "If True, save one set of experiment/reflection files per process, "
            "where each is a concatenated list of all the successful events "
            "examined by that process. If False, output a separate "
            "experiment/reflection file per image (generates a lot of files)."
        ),
    )
    output_logging_dir: str = Field(
        "", description="Directory output log files will be placed"
    )

    # Dispatch settings: dispatch_
    dispatch_index: bool = Field(
        True,
        description=(
            "Attempt to index images. find_spots also needs to be True for "
            "this to work"
        ),
    )
    dispatch_refine: bool = Field(
        False, description="If True, after indexing, refine the experimental models"
    )
    dispatch_integrate: bool = Field(
        True,
        description=(
            "Integrate indexed images. Ignored if index=False or "
            "find_spots=False"
        ),
    )

    # Parallel processing parameters: mp_
    mp_method: str = Field(
        "mpi",  # *multiprocessing sge lsf pbs mpi
        description="The multiprocessing method to use",
    )

    # Spotfinding parameters: spotfinder_
    spotfinder_lookup_mask: Optional[str] = Field(
        None, description="The path to the mask file."
    )
    spotfinder_threshold_dispersion_gain: Optional[float] = Field(
        None,
        description=(
            "Use a flat gain map for the entire detector to act as a "
            "multiplier for the gain set by the format. Cannot be used in "
            "conjunction with lookup.gain_map parameter."
        ),
    )
    spotfinder_threshold_dispersion_sigma_bkgnd: float = Field(
        6,
        description=(
            "The number of standard deviations of the index of dispersion "
            "(variance / mean) in the local area below which the pixel "
            "will be classified as background."
        ),
    )
    spotfinder_threshold_dispersion_sigma_strong: float = Field(
        3,
        description=(
            "The number of standard deviations above the mean in the local "
            "area above which the pixel will be classified as strong."
        ),
    )
    spotfinder_threshold_dispersion_global_threshold: float = Field(
        0,
        description=(
            "The global threshold value. Consider all pixels less than "
            "this value to be part of the background."
        ),
    )
    spotfinder_threshold_dispersion_kernel_size: Tuple[int, int] = Field(
        (6, 6),
        description=(
            "The size of the local area around the spot in which to "
            "calculate the mean and variance. The kernel is given as a box "
            "of size (2 * nx + 1, 2 * ny + 1) centred at the pixel."
        ),
    )
    spotfinder_filter_min_spot_size: Optional[int] = Field(
        3,
        description=(
            "The minimum number of contiguous pixels for a spot to be "
            "accepted by the filtering algorithm."
        ),
    )
    spotfinder_filter_d_min: Optional[float] = Field(
        None,
        description=(
            "The high resolution limit in Angstrom for a pixel to be "
            "accepted by the filtering algorithm."
        ),
    )

    # Indexing parameters: indexing_
    indexing_stills_refine_candidates_with_known_symmetry: bool = Field(
        False,
        description=(
            "If False, when choosing the best set of candidate basis "
            "solutions, refine the candidates in the P1 setting. If True, "
            "after indexing in P1, convert the candidates to the known "
            "symmetry and apply the corresponding change of basis to the "
            "indexed reflections."
        ),
    )
    indexing_stills_refine_all_candidates: bool = Field(
        True,
        description=(
            "If False, no attempt is made to refine the model from initial "
            "basis vector selection. The indexing solution with the best "
            "RMSD is chosen."
        ),
    )
    indexing_known_symmetry_space_group: Optional[str] = Field(
        None, description="Target space group for indexing."
    )
    indexing_known_symmetry_unit_cell: Optional[str] = Field(
        None, description="Target unit cell for indexing."
    )

    # Integration parameters: integration_
    integration_background_simple_outlier_plane_n_sigma: int = Field(
        10,
        description=(
            "The number of standard deviations above the threshold "
            "plane to use in rejecting outliers from background "
            "calculation."
        ),
    )
    integration_summation_detector_gain: float = Field(
        1.0,
        description=(
            "Multiplier for variances after integration of still images. See "
            "Leslie 1999."
        ),
    )

    # Profiling parameters: profile_
    profile_gaussian_rs_centroid_definition: str = Field(
        "com",
        description="The centroid to use as beam divergence (centre of mass or s1)",
    )

    # Refinement options: refinement_
    refinement_reflections_outlier_algorithm: Optional[str] = Field(
        None,
        description=(
            "Outlier rejection algorithm. If auto is selected, the "
            "algorithm is chosen automatically."
        ),
    )

    @validator("output_output_dir", always=True)
    def set_output_dir(cls, output: str, values: Dict[str, Any]) -> str:
        if output == "":
            return os.getenv("LUTE_WORK_DIR", ".")
        return output

    @validator("output_logging_dir", always=True)
    def set_output_log_dir(cls, output: str, values: Dict[str, Any]) -> str:
        if output == "":
            return values["output_output_dir"]
        return output

IndexCrystFELParameters

Bases: ThirdPartyParameters

Parameters for CrystFEL's indexamajig.

There are many parameters, and many combinations. For more information on usage, please refer to the CrystFEL documentation, here: https://www.desy.de/~twhite/crystfel/manual-indexamajig.html

Source code in lute/io/models/sfx_index.py

class IndexCrystFELParameters(ThirdPartyParameters):
    """Parameters for CrystFEL's `indexamajig`.

    There are many parameters, and many combinations. For more information on
    usage, please refer to the CrystFEL documentation, here:
    https://www.desy.de/~twhite/crystfel/manual-indexamajig.html
    """

    class Config(ThirdPartyParameters.Config):
        set_result: bool = True
        """Whether the Executor should mark a specified parameter as a result."""

        long_flags_use_eq: bool = True
        """Whether long command-line arguments are passed like `--long=arg`."""

    executable: str = Field(
        "/sdf/group/lcls/ds/tools/crystfel/0.10.2/bin/indexamajig",
        description="CrystFEL's indexing binary.",
        flag_type="",
    )
    # Basic options
    in_file: Optional[str] = Field(
        "", description="Path to input file.", flag_type="-", rename_param="i"
    )
    out_file: str = Field(
        "",
        description="Path to output file.",
        flag_type="-",
        rename_param="o",
        is_result=True,
    )
    peaks: Optional[str] = Field(
        None,
        description=(
            "Peak finding algorithm, or file type. E.g. peakfinder8 to peak find, "
            "or use cxi for CXI files."
        ),
        flag_type="--",
    )
    geometry: str = Field(
        "", description="Path to geometry file.", flag_type="-", rename_param="g"
    )
    zmq_input: Optional[str] = Field(
        description="ZMQ address to receive data over. `input` and `zmq-input` are mutually exclusive",
        flag_type="--",
        rename_param="zmq-input",
    )
    zmq_subscribe: Optional[str] = Field(  # Can be used multiple times...
        description="Subscribe to ZMQ message of type `tag`",
        flag_type="--",
        rename_param="zmq-subscribe",
    )
    zmq_request: Optional[AnyUrl] = Field(
        description="Request new data over ZMQ by sending this value",
        flag_type="--",
        rename_param="zmq-request",
    )
    asapo_endpoint: Optional[str] = Field(
        description="ASAP::O endpoint. zmq-input and this are mutually exclusive.",
        flag_type="--",
        rename_param="asapo-endpoint",
    )
    asapo_token: Optional[str] = Field(
        description="ASAP::O authentication token.",
        flag_type="--",
        rename_param="asapo-token",
    )
    asapo_beamtime: Optional[str] = Field(
        description="ASAP::O beatime.",
        flag_type="--",
        rename_param="asapo-beamtime",
    )
    asapo_source: Optional[str] = Field(
        description="ASAP::O data source.",
        flag_type="--",
        rename_param="asapo-source",
    )
    asapo_group: Optional[str] = Field(
        description="ASAP::O consumer group.",
        flag_type="--",
        rename_param="asapo-group",
    )
    asapo_stream: Optional[str] = Field(
        description="ASAP::O stream.",
        flag_type="--",
        rename_param="asapo-stream",
    )
    asapo_wait_for_stream: Optional[str] = Field(
        description="If ASAP::O stream does not exist, wait for it to appear.",
        flag_type="--",
        rename_param="asapo-wait-for-stream",
    )
    data_format: Optional[str] = Field(
        description="Specify format for ZMQ or ASAP::O. `msgpack`, `hdf5` or `seedee`.",
        flag_type="--",
        rename_param="data-format",
    )
    basename: bool = Field(
        False,
        description="Remove directory parts of filenames. Acts before prefix if prefix also given.",
        flag_type="--",
    )
    prefix: Optional[str] = Field(
        description="Add a prefix to the filenames from the infile argument.",
        flag_type="--",
        rename_param="asapo-stream",
    )
    nthreads: PositiveInt = Field(
        max(int(os.environ.get("SLURM_NPROCS", len(os.sched_getaffinity(0)))) - 1, 1),
        description="Number of threads to use. See also `max_indexer_threads`.",
        flag_type="-",
        rename_param="j",
    )
    no_check_prefix: bool = Field(
        False,
        description="Don't attempt to correct the prefix if it seems incorrect.",
        flag_type="--",
        rename_param="no-check-prefix",
    )
    highres: Optional[float] = Field(
        description="Mark all pixels greater than `x` has bad.", flag_type="--"
    )
    profile: bool = Field(
        False, description="Display timing data to monitor performance.", flag_type="--"
    )
    temp_dir: Optional[str] = Field(
        description="Specify a path for the temp files folder.",
        flag_type="--",
        rename_param="temp-dir",
    )
    wait_for_file: conint(gt=-2) = Field(
        0,
        description="Wait at most `x` seconds for a file to be created. A value of -1 means wait forever.",
        flag_type="--",
        rename_param="wait-for-file",
    )
    no_image_data: bool = Field(
        False,
        description="Load only the metadata, no iamges. Can check indexability without high data requirements.",
        flag_type="--",
        rename_param="no-image-data",
    )
    # Peak-finding options
    # ....
    # Indexing options
    indexing: Optional[str] = Field(
        description="Comma-separated list of supported indexing algorithms to use. Default is to automatically detect.",
        flag_type="--",
    )
    cell_file: Optional[str] = Field(
        description="Path to a file containing unit cell information (PDB or CrystFEL format).",
        flag_type="-",
        rename_param="p",
    )
    tolerance: str = Field(
        "5,5,5,1.5",
        description=(
            "Tolerances (in percent) for unit cell comparison. "
            "Comma-separated list a,b,c,angle. Default=5,5,5,1.5"
        ),
        flag_type="--",
    )
    no_check_cell: bool = Field(
        False,
        description="Do not check cell parameters against unit cell. Replaces '-raw' method.",
        flag_type="--",
        rename_param="no-check-cell",
    )
    no_check_peaks: bool = Field(
        False,
        description="Do not verify peaks are accounted for by solution.",
        flag_type="--",
        rename_param="no-check-peaks",
    )
    multi: bool = Field(
        False, description="Enable multi-lattice indexing.", flag_type="--"
    )
    wavelength_estimate: Optional[float] = Field(
        description="Estimate for X-ray wavelength. Required for some methods.",
        flag_type="--",
        rename_param="wavelength-estimate",
    )
    camera_length_estimate: Optional[float] = Field(
        description="Estimate for camera distance. Required for some methods.",
        flag_type="--",
        rename_param="camera-length-estimate",
    )
    max_indexer_threads: Optional[PositiveInt] = Field(
        # 1,
        description="Some indexing algos can use multiple threads. In addition to image-based.",
        flag_type="--",
        rename_param="max-indexer-threads",
    )
    no_retry: bool = Field(
        False,
        description="Do not remove weak peaks and try again.",
        flag_type="--",
        rename_param="no-retry",
    )
    no_refine: bool = Field(
        False,
        description="Skip refinement step.",
        flag_type="--",
        rename_param="no-refine",
    )
    no_revalidate: bool = Field(
        False,
        description="Skip revalidation step.",
        flag_type="--",
        rename_param="no-revalidate",
    )
    # TakeTwo specific parameters
    taketwo_member_threshold: Optional[PositiveInt] = Field(
        # 20,
        description="Minimum number of vectors to consider.",
        flag_type="--",
        rename_param="taketwo-member-threshold",
    )
    taketwo_len_tolerance: Optional[PositiveFloat] = Field(
        # 0.001,
        description="TakeTwo length tolerance in Angstroms.",
        flag_type="--",
        rename_param="taketwo-len-tolerance",
    )
    taketwo_angle_tolerance: Optional[PositiveFloat] = Field(
        # 0.6,
        description="TakeTwo angle tolerance in degrees.",
        flag_type="--",
        rename_param="taketwo-angle-tolerance",
    )
    taketwo_trace_tolerance: Optional[PositiveFloat] = Field(
        # 3,
        description="Matrix trace tolerance in degrees.",
        flag_type="--",
        rename_param="taketwo-trace-tolerance",
    )
    # Felix-specific parameters
    # felix_domega
    # felix-fraction-max-visits
    # felix-max-internal-angle
    # felix-max-uniqueness
    # felix-min-completeness
    # felix-min-visits
    # felix-num-voxels
    # felix-sigma
    # felix-tthrange-max
    # felix-tthrange-min
    # XGANDALF-specific parameters
    xgandalf_sampling_pitch: Optional[NonNegativeInt] = Field(
        # 6,
        description="Density of reciprocal space sampling.",
        flag_type="--",
        rename_param="xgandalf-sampling-pitch",
    )
    xgandalf_grad_desc_iterations: Optional[NonNegativeInt] = Field(
        # 4,
        description="Number of gradient descent iterations.",
        flag_type="--",
        rename_param="xgandalf-grad-desc-iterations",
    )
    xgandalf_tolerance: Optional[PositiveFloat] = Field(
        # 0.02,
        description="Relative tolerance of lattice vectors",
        flag_type="--",
        rename_param="xgandalf-tolerance",
    )
    xgandalf_no_deviation_from_provided_cell: Optional[bool] = Field(
        description="Found unit cell must match provided.",
        flag_type="--",
        rename_param="xgandalf-no-deviation-from-provided-cell",
    )
    xgandalf_min_lattice_vector_length: Optional[PositiveFloat] = Field(
        # 30,
        description="Minimum possible lattice length.",
        flag_type="--",
        rename_param="xgandalf-min-lattice-vector-length",
    )
    xgandalf_max_lattice_vector_length: Optional[PositiveFloat] = Field(
        # 250,
        description="Minimum possible lattice length.",
        flag_type="--",
        rename_param="xgandalf-max-lattice-vector-length",
    )
    xgandalf_max_peaks: Optional[PositiveInt] = Field(
        # 250,
        description="Maximum number of peaks to use for indexing.",
        flag_type="--",
        rename_param="xgandalf-max-peaks",
    )
    xgandalf_fast_execution: bool = Field(
        False,
        description="Shortcut to set sampling-pitch=2, and grad-desc-iterations=3.",
        flag_type="--",
        rename_param="xgandalf-fast-execution",
    )
    # pinkIndexer parameters
    # ...
    # asdf_fast: bool = Field(False, description="Enable fast mode for asdf. 3x faster for 7% loss in accuracy.", flag_type="--", rename_param="asdf-fast")
    # Integration parameters
    integration: str = Field(
        "rings-nocen", description="Method for integrating reflections.", flag_type="--"
    )
    fix_profile_radius: Optional[float] = Field(
        description="Fix the profile radius (m^{-1})",
        flag_type="--",
        rename_param="fix-profile-radius",
    )
    fix_divergence: Optional[float] = Field(
        0,
        description="Fix the divergence (rad, full angle).",
        flag_type="--",
        rename_param="fix-divergence",
    )
    int_radius: str = Field(
        "4,5,7",
        description="Inner, middle, and outer radii for 3-ring integration.",
        flag_type="--",
        rename_param="int-radius",
    )
    int_diag: str = Field(
        "none",
        description="Show detailed information on integration when condition is met.",
        flag_type="--",
        rename_param="int-diag",
    )
    push_res: str = Field(
        "infinity",
        description="Integrate `x` higher than apparent resolution limit (nm-1).",
        flag_type="--",
        rename_param="push-res",
    )
    overpredict: bool = Field(
        False,
        description="Over-predict reflections. Maybe useful with post-refinement.",
        flag_type="--",
    )
    cell_parameters_only: bool = Field(
        False, description="Do not predict refletions at all", flag_type="--"
    )
    # Output parameters
    no_non_hits_in_stream: bool = Field(
        False,
        description="Exclude non-hits from the stream file.",
        flag_type="--",
        rename_param="no-non-hits-in-stream",
    )
    copy_hheader: Optional[str] = Field(
        description="Copy information from header in the image to output stream.",
        flag_type="--",
        rename_param="copy-hheader",
    )
    no_peaks_in_stream: bool = Field(
        False,
        description="Do not record peaks in stream file.",
        flag_type="--",
        rename_param="no-peaks-in-stream",
    )
    no_refls_in_stream: bool = Field(
        False,
        description="Do not record reflections in stream.",
        flag_type="--",
        rename_param="no-refls-in-stream",
    )
    serial_offset: Optional[PositiveInt] = Field(
        description="Start numbering at `x` instead of 1.",
        flag_type="--",
        rename_param="serial-offset",
    )
    harvest_file: Optional[str] = Field(
        description="Write parameters to file in JSON format.",
        flag_type="--",
        rename_param="harvest-file",
    )

    @validator("in_file", always=True)
    def validate_in_file(cls, in_file: str, values: Dict[str, Any]) -> str:
        if in_file == "":
            filename: Optional[str] = read_latest_db_entry(
                f"{values['lute_config'].work_dir}", "FindPeaksPyAlgos", "out_file"
            )
            if filename is None:
                exp: str = values["lute_config"].experiment
                run: int = int(values["lute_config"].run)
                tag: Optional[str] = read_latest_db_entry(
                    f"{values['lute_config'].work_dir}", "FindPeaksPsocake", "tag"
                )
                out_dir: Optional[str] = read_latest_db_entry(
                    f"{values['lute_config'].work_dir}", "FindPeaksPsocake", "outDir"
                )
                if out_dir is not None:
                    fname: str = f"{out_dir}/{exp}_{run:04d}"
                    if tag is not None:
                        fname = f"{fname}_{tag}"
                    return f"{fname}.lst"
            else:
                return filename
        return in_file

    @validator("out_file", always=True)
    def validate_out_file(cls, out_file: str, values: Dict[str, Any]) -> str:
        if out_file == "":
            expmt: str = values["lute_config"].experiment
            run: int = int(values["lute_config"].run)
            work_dir: str = values["lute_config"].work_dir
            tag: Optional[str] = read_latest_db_entry(
                f"{values['lute_config'].work_dir}", "FindPeaksPyAlgos", "tag"
            )
            if tag is None:
                tag: Optional[str] = read_latest_db_entry(
                    f"{values['lute_config'].work_dir}", "FindPeaksPsocake", "tag"
                )
            fname: str = f"{expmt}_r{run:04d}"
            if tag is not None:
                fname = f"{fname}_{tag}"
            return f"{work_dir}/{fname}.stream"
        return out_file

Config

Bases: Config

Source code in lute/io/models/sfx_index.py

class Config(ThirdPartyParameters.Config):
    set_result: bool = True
    """Whether the Executor should mark a specified parameter as a result."""

    long_flags_use_eq: bool = True
    """Whether long command-line arguments are passed like `--long=arg`."""

long_flags_use_eq: bool = True class-attribute instance-attribute

Whether long command-line arguments are passed like --long=arg.

set_result: bool = True class-attribute instance-attribute

Whether the Executor should mark a specified parameter as a result.